Search engine enhancement using mined implicit links

ABSTRACT

An implicit links enhancement system and method for search engines that generates implicit links obtained from mining user access logs to facilitate enhanced local searching of web sites and intranets. Embodiments of the implicit links search enhancement system and method includes extracting implicit links by mining users&#39; access patterns and then using a modified link analysis algorithm to re-rank search results obtained from traditional search engines. More specifically, embodiments of the method include extracting implicit links from a user access log, generating an implicit links graph from the extracted implicit links, and computing page rankings using the implicit links graph. The implicit links are extracted from the log using a two-item sequential pattern mining technique. Search results obtained from a search engine are re-ranked based on an implicit links analysis performed using an updated implicit links graph, a modified re-ranking formula, and at least one re-ranking technique.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional application of U.S. applicationSer. No. 10/676,794 filed Sep. 30, 2003, now allowed, which isincorporated by reference in its entirety as if fully set forth herein.

BACKGROUND

Search engines are vital for helping a user find specific information inthe vast expanse of the World Wide Web (WWW or Web). Because the Webcontinues to grow at a phenomenal rate, it would be virtually impossibleto locate anything on the Web without knowing a specific address if notfor search engines. Generally, a search engine refers to a system thatmaintains an index structure of a collection of documents to efficientlygenerate a list of documents that contain specified keywords and ranksthe document list according to a relevance measurement. Global searchengines, which are popular and widespread, are used to search the entireWeb, while local search engines are used to search web sites andintranets.

Many types of popular and effective global search engines use linkanalysis to quickly and efficiently search the entire Web. These searchengines analyze links to rank web sites (or pages) according to, amongother things, the quality and quantity of other sites that are linked tothem. In general, a link (in a hypertext context such as the Web) is areference to another page or site. When a user clicks on a link within asite, the user is taken to the other site. In theory, the more sitesthat link are linked to a certain site, the higher ranking the searchengine will give the particular web site because more links indicates ahigher level of popularity among users.

Link analysis techniques (such as HITS and PageRank) are widely used toanalyze the importance of a page. In both the HITS and PageRanktechniques, the Web is represented a directed graph G={V, E}, where Vstands for web-pages w_(i), and E stands for the hyperlinks l_(i,j)within two pages. For the HITS technique, each web-page w_(i) has both ahub score h_(i) and an authority score a_(i). The hub score of w_(i) isthe sum of all the authority scores of pages that are pointed by w_(i);the authority score of w_(i) is the sum of all the hub scores of pagesthat point to w_(i), as shown in the following equations.

${a_{i} = {\sum\limits_{j:{l_{j,i} \in E}}\; h_{j}}},{h_{i} = {\sum\limits_{j:{l_{j,i} \in E}}\; a_{j}}}$The final authority and hub scores of every web page are obtainedthrough an iterative update process.

PageRank is a core algorithm of the popular Google search engine(http://www.google.com.). PageRank measures the importance of web pages.specifically, PageRank uses the whole linkage graph of the Web tocompute universal query-independent rank value for each page. A users'browsing model is modeled as a random surfing model. This model assumesthat a user either follows a link from a current page or jumps to arandom page in the graph. The PageRank of a page w_(i) then is computedby the following equation:

${{PR}\left( w_{i} \right)} = {\frac{ɛ}{n} + {\left( {1 - ɛ} \right) \times {\sum\limits_{l_{j,i} \in E}\;{{{PR}\left( w_{j} \right)}/{{outdegree}\left( w_{j} \right)}}}}}$where ε is a dampening factor, which is usually set between 0.1 and 0.2,n is the number of nodes in G, and out-degree (w_(j)) is the number ofthe edges leaving page w_(j) (i.e., the number of hyperlinks on pagew_(j)). The PageRank can be computed by an iterative algorithm andcorresponds to the primary eigenvector of a matrix derived fromadjacency matrix of the available portion of the Web.

Although these global search engines work relatively well for searchingthe Web, they are unavailable for local searches, such as searches of aweb site or an intranet. A web site can be thought of as a closed spaceon the web where data and information are available to a user. Forexample, web sites include enterprise portals (allowing document accessand product information), server providers (including access to news andmagazines), education institutions providing online courses and documentaccess, and user groups, to name a few. Frequently, to obtain specificand up-to-date information, a user will often go directly to a specificweb site and conduct site search. However, in addition to beingunavailable for local searches, global search engines are alsoimpractical for local searching because the link structure of a web siteand intranet is different from the Web. In the closed sub-space of a website or intranet local search engines must used.

Existing local (or small web) search engines generally use the same linkanalysis technology as those used in global search engines. However,their performances are problematic. Some current site-specific searchengines fail to deliver all the relevant content, instead returning toomuch irrelevant content to meet the user's information needs.Furthermore, little benefit is obtained from the use of link-basedmethods.

One problem with using link analysis for local searches is that the linkstructure of a small web is different from the global Web. As explainedin detail below, for the global Web, existing link analysis usesexplicit links to a certain site to determine the ranking of the site.While this recommendation assumption is generally correct for the Web,it is commonly invalid for a Web site or intranet. In general, this isbecause there are relatively few explicit links and the links arecreated by a small number of authors whose purpose is to organize thecontents into a hierarchical structure. Thus, in general the authorityof pages is not captured correctly by link analysis.

Since direct application of link analysis in a local searching isimpractical, some systems focus on usage information. For example,DirectHit (http://www.directhit.com) harnesses millions of humandecisions by millions of daily Internet searchers to provide morerelevant and better organized search results. DirectHit's site rankingsystem, which is based on the concepts of “click popularity” and“stickiness,” is currently used by Lycos, Hotbot, MSN, Infospace,About.com and several other search engines. The underlying assumption isthat the more a web-page is visited, the higher it is ranked accordingto particular queries. These usage-based search engines, however, haverestrictions. In particular, one problem is that the technique requireslarge amounts of user logs and only works for some popular queries.Another problem is that it is easy to fall into a quick positivefeedback loop when access to a popular resource leads to its higherrank. This in turn leads to an even higher number accesses to it.

There are also some techniques that operate by combining usage data inlink analysis. One such technique utilizes usage data to modify theadjacency matrix in the HITS technique. Namely, the adjacency matrix Mis replaced with a link matrix M′, which assign the weight between nodes(pages) based on a user's usage data collected from web-server logs.

One problem, however, with this method is that it does not separate theuser logs into sessions based on their tasks. This makes the techniquevulnerable to noise data that inevitably will be introduced into thelink matrix. Another problem is that Web users often follow differentpaths to reach a same goal. If only adjacent pages are treated asrelated, the underlying relationship will not be discovered.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Embodiments of the implicit links search enhancement system and methodincludes generating implicit links obtained from mining user access logsto facilitate enhanced local searching of Web sub-space (such as websites and intranets). Embodiments of the implicit links searchenhancement system and method augments traditional link analysis searchengines popular for global Web searches and makes them available forlocal searching of Web sub-space. Embodiments of the implicit linkssearch enhancement system and method extracts implicit links in additionto explicit links and filters out unimportant links to achieve improvedsearch results. The initial search results obtained with a traditionlink analysis search engine then are updated based on the informationprovided by the implicit links search enhancement system and method.

Embodiments of the implicit links search enhancement method includesgenerating implicit links from a user access log. The implicit links areimplicit recommendation links. All probably implicit links then areextracted from the user access log using a two-item sequential patternmining technique. This technique includes using a gliding window to findordered pairs of implicit links or pages. An implicit links graph isconstructed using the extracted implicit links. Two-item sequentialpatterns also are generated from the implicit links and are used toupdate the implicit links graph. Updated rankings of the search resultsare made using the updated implicit links graph and a modified implicitlinks analysis.

In some embodiments the user access log is pre-processed. Thispre-processing includes data cleaning, session identification, andconsecutive repetition elimination. Data cleaning is performed byfiltering out any access entries for embedded objects, such as imagesand scripts. Browsing sessions are identified by the Internet protocol(IP) address and assumes consecutive accesses from the same IP addressduring a time interval are from the same user. Consecutive repetitionelimination removes IP addresses whose page hits count exceeds somethreshold. After pre-processing, the user access log is segmented intoindividual browsing sessions. Each browsing session is identified by itsuser identification and pages in a browsing path ordered by timestamp.The ordered pairs are generated from the segmented user access log.First, a gliding window size is defined. Next, the gliding window isapplied to each individual browsing session along the browsing path togenerate all possible ordered pairs and their probabilities.

In still other embodiments, the ordered pairs are filtered to removeunnecessary links. In particular, a frequency for each of the orderedpairs is determined. In some embodiments, a minimum support threshold isdefined and applied to the frequency of each of the ordered pairs. If afrequency is below the minimum support threshold, the associated orderedpair is discarded. Otherwise, the ordered pair is kept and used toupdate the implicit links graph.

A modified links analysis technique is used to re-rank initial searchresults. The modified links analysis technique uses the updated implicitlinks graph, a modified re-ranking formula, and at least one of twore-ranking techniques. The modified re-ranking formula is a re-rankingformula from PageRank but having novel modifications. One of thesemodifications is that the traditional PageRank only uses 0 or 1 valuesfor each entry in the adjacency matrix, representing the existence of ahyperlink, while the modified re-ranking formula accommodates anyfloating point values between 0 and 1. The modified links analysistechnique uses at least one of two re-ranking techniques: (a) anorder-based re-ranking technique; and (b) a score-based re-rankingtechnique. In some embodiments, the order-based re-ranking technique ispreferred. The order-based re-ranking technique uses is based on therank order of pages. The order-based technique is a linear combinationof a position of a page in two lists, where one list is sorted bysimilarity and the other list is sorted by PageRank values. Thescore-based technique uses a linear combination of a content-basedsimilarity score and a PageRank value of all web pages.

Embodiments of the implicit links search enhancement system are designedto work in unison with a search engine to provide improved searchresults. Embodiments of the system include a user access logpre-processing module, which performs pre-processing of the user accesslog, and a user access log segmentation module, which segments thepre-processed log into individual browsing sessions. Embodiments of thesystem also include an ordered pairs generator and a filter module. Theordered pairs generator generates all possible ordered pairs of implicitlinks and pages from each of the individual browsing sessions. Thefilter module filters the extracted ordered pairs to cull anyinfrequently occurring links and make the search results re-ranking moreaccurate. Embodiments of the implicit links search enhancement systemfurther include an updated module, which updates an implicit links graphusing the filtered ordered pairs, and a re-ranking module. There-ranking module uses the updated implicit links graph, a modifiedre-ranking formula, and at least one re-ranking technique to re-ranksearch results from a search engine into an improved search result.

It should be noted that alternative embodiments are possible, and thatsteps and elements discussed herein may be changed, added, oreliminated, depending on the particular embodiment. These alternativeembodiments include alternative steps and alternative elements that maybe used, and structural changes that may be made, without departing fromthe scope of the invention.

DRAWINGS DESCRIPTION

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 is a block diagram illustrating a general overview of anexemplary implementation of embodiments of the implicit links searchenhancement system and method disclosed herein.

FIG. 2 illustrates an example of a suitable computing system environmentin which embodiments of the implicit links search enhancement system andmethod shown in FIG. 1 may be implemented.

FIG. 3 is a block diagram illustrating the details of an exemplaryimplementation of embodiments of the implicit links search enhancementsystem shown in FIG. 1.

FIG. 4 is a general flow diagram illustrating the general operation ofembodiments of the implicit links search enhancement method of theimplicit links search enhancement system shown in FIGS. 1 and 3.

FIG. 5 is a detailed flow diagram illustrating the operation ofembodiments of the implicit links search enhancement method shown inFIG. 4 and used in the implicit link search enhancement system 100 shownin FIGS. 1 and 3.

FIG. 6 is a detailed flow diagram illustrating the operation of the useraccess log pre-processing module shown in FIG. 3.

FIG. 7 is a detailed flow diagram illustrating the operation of the useraccess log segmentation module shown in FIG. 3.

FIG. 8 is a detailed flow diagram illustrating the operation of theordered pairs generator shown in FIG. 3.

FIG. 9 is a detailed flow diagram illustrating the operation of thefilter module shown in FIG. 3.

FIG. 10 is a detailed flow diagram illustrating the operation of there-ranking module shown in FIG. 3.

DETAILED DESCRIPTION

In the following description of embodiments of the implicit links searchenhancement system and method reference is made to the accompanyingdrawings, which form a part thereof, and in which is shown by way ofillustration a specific example whereby embodiments of the implicitlinks search enhancement system and method may be practiced. It is to beunderstood that other embodiments may be utilized and structural changesmay be made without departing from the scope of the claimed subjectmatter.

I. Introduction

Conventional link analysis techniques (such as PageRank and HITS) useeigenvector calculations to identify authoritative pages based onhyperlink structures. The intuition is that a page with high in-degreeis highly recommended, and should have a high rank score. However, thereis a basic assumption underlying those link analysis algorithms: namely,that the whole Web is a citation graph, and each hyperlink represents acitation or recommendation relationship.

Formally, there is the following recommendation assumption: a hyperlinkin page X pointed to page Y stands for the recommendation of page Y bythe author of page X. For the global Web, the recommendation assumptionis generally correct because hyperlinks encode a considerable amount ofauthors' judgment. Of course, some hyperlinks are created not for therecommendation purpose, but their influence could be filtered or reducedto an ignorable level.

The recommendation assumption, however, commonly is invalid in the caseof a small web. The majority of hyperlinks in a small web are more“regular” than that in the global Web. Most links are from a parent nodeto children nodes, between sibling nodes, or from leaves to the root(e.g. “Back to Home”). The reason is primarily because hyperlinks in asmall web are created by a small number of authors. Moreover, thepurpose of the hyperlinks is usually to organize the content into ahierarchical or linear structure. Thus, the in-degree measure does notreflect the authority of pages, making the existing link analysisalgorithms not suitable for small web search.

In a small web, hyperlinks could be divided into navigational links andrecommendation links. The latter is useful for link analysis to enhancesearch. However, only filtering out navigational links from allhyperlinks is inadequate because the remaining recommendation links areincomplete. In other words, there are many implicit recommendation links(hereafter called “implicit links” for short) in a small web that couldbe discovered by mining user access pattern.

II. General Overview

Conventional link analysis techniques (such as PageRank) do not workwell when directly applied to analyze the link structure in a small websuch as a web site or an intranet. This is because the recommendationassumption for hyperlinks used in these conventional link analysistechniques is commonly invalid in a small web or intranet. The implicitlinks search enhancement system and method described herein augmentsconventional search engines to make them more efficient and accurate.Specifically, the implicit links search enhancement system and methodincludes constructing implicit links by mining users' access patternsand then using a modified link analysis algorithm to re-rank searchresults obtained from traditional search engines. Experimental resultsobtained in a working example illustrate that the implicit links searchenhancement system and method effectively improves search performance ofexisting search engines.

FIG. 1 is a block diagram illustrating a general overview of anexemplary implementation of embodiments of the implicit links searchenhancement system and method disclosed herein. Embodiments of theimplicit links search enhancement system 100 typically are implementedin a computing environment 110. This computing environment 110, which isdescribed in detail below, includes computing devices (not shown). Ingeneral, the implicit links search enhancement system 100 augments thesearch results obtained by a traditional search engine (such as a sitesearch engine 120) based on an implicit link analysis.

Initially, a user sends a user query 130 to the site search engine 120.In this exemplary implementation, the site may be a web site or anintranet. The site search engine 120 obtains pages 140 (such as webpages) and indexes those pages (box 150). Next, the inverted index 160is obtained by the site search engine 120. Using existing searchtechniques, the site search engine 120 obtains and ranks initial searchresults.

The implicit links search enhancement system 100 obtains data from auser access log 170 and performs an implicit link analysis on the log170. This analysis is described in detail below. The implicit linkssearch enhancement system 100 outputs page rankings 180 based on theanalysis performed by the implicit links search engine 100. The sitesearch engine 120 uses these page rankings to update the initial searchresults and output updated search results 190 to the user in response toa query.

III. Exemplary Operating Environment

Embodiments of the implicit links search enhancement system 100 andmethod are designed to operate in a computing environment. The followingdiscussion is intended to provide a brief, general description of asuitable computing environment in which embodiments of the implicitlinks search enhancement system 100 and method may be implemented.

FIG. 2 illustrates an example of a suitable computing system environmentin which embodiments of the implicit links search enhancement system 100and method shown in FIG. 1 and FIGS. 3-10 may be implemented. Thecomputing system environment 200 is only one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the invention. Neither shouldthe computing environment 200 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment.

Embodiments of the implicit links search enhancement system 100 andmethod are operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell known computing systems, environments, and/or configurations thatmay be suitable for use with embodiments of the implicit links searchenhancement system 200 and method include, but are not limited to,personal computers, server computers, hand-held (including smartphones),laptop or mobile computer or communications devices such as cell phonesand PDA's, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like.

Embodiments of the implicit links search enhancement system 100 andmethod may be described in the general context of computer-executableinstructions, such as program modules, being executed by a computer.Generally, program modules include routines, programs, objects,components, data structures, etc., that perform particular tasks orimplement particular abstract data types. Embodiments of the implicitlinks search enhancement system 100 and method may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules may be located inboth local and remote computer storage media including memory storagedevices. With reference to FIG. 2, an exemplary system for embodimentsof the implicit links search enhancement system 100 and method includesa general-purpose computing device in the form of a computer 210.

Components of the computer 210 may include, but are not limited to, aprocessing unit 220 (such as a central processing unit, CPU), a systemmemory 230, and a system bus 221 that couples various system componentsincluding the system memory to the processing unit 220. The system bus221 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus also known as Mezzanine bus.

The computer 210 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the computer 210 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data.

Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computer 210. By way of example, andnot limitation, communication media includes wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 230 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 231and random access memory (RAM) 232. A basic input/output system 233(BIOS), containing the basic routines that help to transfer informationbetween elements within the computer 210, such as during start-up, istypically stored in ROM 231. RAM 232 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 220. By way of example, and notlimitation, FIG. 2 illustrates an operating system 234, applicationprograms 235, other program modules 236, and program data 237.

The computer 210 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 2 illustrates a hard disk drive 241 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 251that reads from or writes to a removable, nonvolatile magnetic disk 252,and an optical disk drive 255 that reads from or writes to a removable,nonvolatile optical disk 956 such as a CD ROM or other optical media.

Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 241 is typically connectedto the system bus 221 through a non-removable memory interface such asinterface 240, and magnetic disk drive 251 and optical disk drive 255are typically connected to the system bus 221 by a removable memoryinterface, such as interface 250.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 2, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 210. In FIG. 2, for example, hard disk drive 241 is illustratedas storing operating system 244, application programs 245, other programmodules 246, and program data 247. Note that these components can eitherbe the same as or different from operating system 234, applicationprograms 235, other program modules 236, and program data 237. Operatingsystem 244, application programs 245, other program modules 246, andprogram data 247 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation (or data) into the computer 210 through input devices suchas a keyboard 262, pointing device 261, commonly referred to as a mouse,trackball or touch pad, and a touch panel or touch screen (not shown).

Other input devices (not shown) may include a microphone, joystick, gamepad, satellite dish, scanner, radio receiver, or a television orbroadcast video receiver, or the like. These and other input devices areoften connected to the processing unit 220 through a user inputinterface 260 that is coupled to the system bus 221, but may beconnected by other interface and bus structures, such as, for example, aparallel port, game port or a universal serial bus (USB). A monitor 291or other type of display device is also connected to the system bus 221via an interface, such as a video interface 290. In addition to themonitor, computers may also include other peripheral output devices suchas speakers 297 and printer 296, which may be connected through anoutput peripheral interface 295.

The computer 210 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer280. The remote computer 280 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 210, although only a memory storage device 281 has beenillustrated in FIG. 2. The logical connections depicted in FIG. 2include a local area network (LAN) 271 and a wide area network (WAN)273, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 210 is connectedto the LAN 271 through a network interface or adapter 270. When used ina WAN networking environment, the computer 210 typically includes amodem 272 or other means for establishing communications over the WAN273, such as the Internet. The modem 272, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 260, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 210, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 2 illustrates remoteapplication programs 285 as residing on memory device 281. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

IV. System Components

FIG. 3 is a block diagram illustrating the details of an exemplaryimplementation of embodiments of the implicit links search enhancementsystem 100 shown in FIG. 1. As shown in FIG. 3, embodiments of theimplicit links search enhancement system 100 obtains data from the useraccess log 170 and outputs page rankings based on an implicit linksanalysis 180. Embodiments of the implicit links search enhancementsystem 100 includes a number of modules. The function of these modulesis described in detail below. The modules located in the implicit linkssearch enhancement system 100 include a user access log preprocessingmodule 300 and a user access log segmentation module 310. The useraccess log preprocessing module 300 preprocesses the user access log 170such that the data is cleaned and users are identified. The preprocesseddata is input for the user access log segmentation module 310, whichsegments the data into individual browsing sessions.

Embodiments of the implicit links search enhancement system 100 alsoincludes an ordered pairs generator 320 and a filter module 330. Theordered pairs generator 320 generates all possible ordered pairs fromeach of the individual browsing sessions. The ordered pairs processed bythe filter module 330 to filter out any infrequently-occurring orderedpairs. The implicit links search enhancement system 100 also includes anupdate module 340 and a re-ranking module 350. The remaining orderedpairs from the filter module 330 are input to the update module 340where the pairs are used to update an implicit link graph. The graph isused by the re-ranking module 350 to re-rank the search results(including pages). The output from the implicit links search enhancementsystem 100 are the updated page rankings 180.

V. Operational Overview

Embodiments of the implicit links search enhancement system 100disclosed herein uses the implicit links search enhancement method toenable improved search performance of a traditional search engine. FIG.4 is a general flow diagram illustrating the general operation ofembodiments of the implicit links search enhancement method of theimplicit links search enhancement system 100 shown in FIGS. 1 and 3. Themethod begins by segmenting a user access log into a plurality ofdifferent browsing sessions (box 400). Next, implicit links areextracted from the sessions (box 410). In a preferred embodiment, theimplicit links are extracted using a two-item sequential pattern miningtechnique. As explained below, this mining technique uses a glidingwindow to move over each path in the user access log and generate allordered pairs.

An implicit links graph is generated using the extracted implicit links(box 420). As discussed below, this implicit links graph is used inplace of an explicit link graph used in conventional link analysistechniques. Based on the implicit link graph, a generative model for auser access log can be defined. Given the user access log, thisgenerative model is used to estimate parameters for the log, includingthe implicit links and their probabilities. Moreover, two-itemsequential patterns generated from this mining technique above can beused to update the implicit link graph. Finally, page rankings arecomputed using the implicit links graph (box 430).

FIG. 5 is a detailed flow diagram illustrating the operation ofembodiments of the implicit links search enhancement method shown inFIG. 4 and used in the implicit link search enhancement system 100 shownin FIGS. 1 and 3. Embodiments of the implicit links search enhancementmethod begins by pre-processing a user access log (box 500). Thispre-processing includes cleaning, identification and elimination ofredundancies of data in the user access log. Next, the log is segmentedinto individual browsing sessions (box 510). Each browsing sessionincludes a user identification and pages visited in chronological order.Ordered pairs of pages then are generated from the segmented log (box520).

The ordered pairs of pages then are filtered to remove any pairs thatare infrequently occurring (box 530). As explained in detail below, thisfiltering is performed using a minimum support threshold. This generatestwo-item sequential patterns, which are used to update an implicit linkgraph (box 540). Next, using a modified link analysis technique, thesearch results are re-ranked (box 550). As explained in detail below,the modified link analysis technique includes a modified re-rankingformula and at least one of two types of re-ranking techniques.

VI. Operational Details

Generally a web space can be modeled as a directed graph G=(V, E) whereV={w_(i)|1≦i≦n} is the set of vertices representing all the pages in theweb, and E encompasses the set of links between the pages. l_(i,j)εE isused to denote that there exists a link between the page w_(i) andw_(j). The implicit links search enhancement system and methodconstructs an implicit link graph instead of the traditional explicitlink graph in a small web sub-space. This implicit links graph is aweighted directed graph G′=(V, E′), where V is same as above, exceptthat E′ encompasses the implicit links between pages. Furthermore, eachimplicit link l_(i,j)εE′ is associated with a new parameterP(w_(j)|w_(i)) denoting the conditional probability of the page w_(j) tobe visited given current page w_(i).

Embodiments of the implicit links search enhancement system and methoddisclosed herein extracts implicit links E′ by analyzing the observedusers' browsing behaviors contained in a user access log. The main ideais to assume that E′ controls how the user traverses in the small web.Based on the implicit link graph G′ and explicit link graph G, it can beassumed that there exists a generative model for the user access log.The entire user access log consists of a number of browsing sessionsS={s₁, s₂, s₃, . . . ). Each session is generated by the followingsteps:

-   -   (1) Randomly select a page w_(i) from V as the starting point;    -   (2) Generate an implicit path (w_(i), w_(j), w_(k), . . . )        according to the implicit links E′ and the associated        probabilities, where it is assumed each selection of implicit        link is independent on previous selections;    -   (3) For each pair of adjacent pages w_(i) and w_(j) in the        implicit path, randomly select a set of in-between pages w_(x1),        x_(x2), . . . , w_(xm) according to the explicit links E to form        an explicit path (w_(i), w_(x1), w_(x2), . . . , w_(xm), w_(j)).

In other words, the model controls the generation of the user access logbased on implicit links and explicit links. The final user access logcontains abundant information on all implicit links. Thus, implicitlinks can be extracted by analyzing the observed explicit paths in theuser access log.

As discussed above with regard to FIG. 3, embodiments of the implicitlinks search enhancement system 100 contains a number of modules. Theoperational details of these modules now will be discussed.

FIG. 6 is a detailed flow diagram illustrating the operation of the useraccess log preprocessing module 300 shown in FIG. 3. The user access logpreprocessing module 300 initially inputs a user access log (box 600)and then performs data cleaning on the log (box 610). Data cleaning isdone by filtering out any access entries for embedded objects, such asimages and scripts. Next, session identification is performed (box 620).All users are distinguished by their IP address. This assumes thatconsecutive accesses from the same IP address during a certain timeinterval are from a same user.

Next, consecutive repetition elimination is performed (box 630). Thiselimination handles the case of multiple users that have the same IPaddress. In particular, IP addresses whose page hits count exceeds somethreshold are removed. The consecutive entries are then grouped into abrowsing session. Finally, the processed user access log is sent asoutput (box 640).

FIG. 7 is a detailed flow diagram illustrating the operation of the useraccess log segmentation module 310 shown in FIG. 3. The processed useraccess log is received an input (box 700). Next, each individualbrowsing session in the processed user access log is identified (box710). This identification is in terms of the user identification and thepages in a chronological order. Specifically, each browsing session isdefined as S={s₁, s₂, . . . , s_(m)), where s_(i)=(u_(i): p_(i1),p_(i2), . . . , p_(ik)). Here, u_(i) is the user identification andp_(ij) are the pages in a browsing path ordered by timestamp. Next, thesegmented user access log is sent as output (box 720).

FIG. 8 is a detailed flow diagram illustrating the operation of theordered pairs generator 320 shown in FIG. 3. The ordered pairs generator320 uses a two-item sequential pattern mining technique to discover (orgenerate) possible implicit links. This technique uses a gliding windowto move over each explicit path, generating all the ordered pairs andcounting the occurrence of each distinct pair. The gliding window sizerepresents the maximum interval a user clicks between the source pageand the target page. For example, for an explicit path (w_(i1), w_(i2),w_(i3), . . . , w_(ik)), the technique generates pairs (i1, i2), (i1,i3), . . . , (i1, ik), (i2, i3), . . . , (i2, ik), . . . If one of thepairs (such as (i, j)) corresponds to an implicit link (l_(i,j)εE′),paths of the pattern (w_(i), . . . , w_(j)) should occur frequently inthe log, with different in-between pages.

Referring to FIG. 8, initially, the individual browsing session from thesegmented user access log are received as input (box 800). Next, agliding window size is defined (box 810). The gliding window is used tomove over the path within each session to generate ordered pairs ofpages. The gliding window size represents the maximum intervals usersclick between a source page and a target page. The gliding window thenis applied to each individual browsing session (box 820). Next allpossible ordered pairs are generated from each of the individualbrowsing sessions (box 830). The order pairs then are sent as output(box 840).

FIG. 9 is a detailed flow diagram illustrating the operation of thefilter module 330 shown in FIG. 3. All possible ordered pairs and theirfrequency are calculated from all the browsing sessions S, andinfrequent occurrences are filtered by a minimum support threshold.Precisely, the support of an item i, denoted as supp(i), refers to thepercentage of the sessions that contain the item i. The support of atwo-item pair (i, j), denoted supp(i, j), is defined in a similar way. Atwo-item ordered pair is frequent if its support supp(i, j)≧min-supp,where min_supp is a user specified number.

Referring to FIG. 9, the ordered pairs are receive as input (box 900)and the frequency of each of the ordered pairs is determined (box 910).The minimum support threshold is defined (box 920) and applied to thefrequency of each of the order pairs (box 930). A determination then ismade whether the frequency is above the threshold (box 940). If not,then the ordered pair is discarded (box 950). Otherwise, the orderedpair is kept (box 960). The filtered two-item sequential patterns thenare sent as output (box 970).

After the two-item sequential patterns are generated, they are used toupdate the implicit link graph G′=(V, E′) described previously. All theweights of edges in E′ are initialized to zero. For each two-itemsequential pattern (i, j), its support supp(i, j) is added to the weightof the edge l_(i, j). All of the weights are normalized to represent thereal probability. The resulting graph subsequently is used in a modifiedlink analysis algorithm.

FIG. 10 is a detailed flow diagram illustrating the operation of there-ranking module 350 shown in FIG. 3. In general, the re-ranking module350 inputs the updated implicit link graph or structure (box 1000).Next, an adjacency matrix is defined to describe the implicit link graph(box 1010). A modified re-ranking formula is defined in terms of theadjacency matrix (box 1020). Search results are re-ranked using amodified link analysis technique (box 1030). The modified link analysistechnique includes using the modified re-ranking formula and at leastone type of re-ranking technique. One type of re-ranking technique is ascore based re-ranking technique. Another type of re-ranking techniqueis an order based re-ranking technique. In a preferred embodiment, theorder-based re-ranking technique is used. The re-ranked search resultsthen are sent as output (box 1040).

More specifically, after inputting the implicit link graph or structure,a modified link analysis technique is used to re-rank the search resultsobtained from a traditional search engine. In a preferred embodiment,the modified link analysis technique is based on the PageRank linkanalysis algorithm that is modified with novel modifications. Asmentioned above, the traditional PageRank algorithm is described in apaper by L. Page et al. entitled “The PageRank citation ranking:bringing order to the Web”.

The modified PageRank links analysis technique works as follows. First,an adjacency matrix is constructed to describe the implicit links graph.In particular, assume the graph contains n pages. The n×n adjacencymatrix is denoted by A and the entries A[i, j] is defined to be theweight of the implicit links l_(i, j). The adjacency matrix is used tocompute the rank score of each page. In an “ideal” form, the rank scorePR_(i) of page w_(i) is evaluated by a function on the rank scores ofall the pages that point to page w_(i):

${PR}_{i} = {\sum\limits_{j:{l_{ji} \in E}}\;{{PR}_{j} \cdot {A\left\lbrack {j,i} \right\rbrack}}}$This recursive definition gives each page a fraction of the rank of eachpage pointing to it—inversely weighted by the strength of the links ofthat page. The above equation can be written in the form of matrix as:{right arrow over (PR)}={right arrow over (APR)}

In practice, however, many pages have no in-links (or the weight of themis 0), and the eigenvector of the above equation is mostly zero.Therefore, the basic model is modified to obtain an “actual model” usinga random walk technique. In particular, upon browsing a web-page, havinga probability 1−ε, a user randomly chooses one of the links on thecurrent page and jumps to a linked page, having a probability parameterε. The user “resets” by jumping to a web-page picked uniformly and atrandom from the collection. Therefore, the random walk technique is usedto modify the ranking formula to the following form:

${PR}_{i} = {\frac{ɛ}{n} + {\left( {1 - ɛ} \right){\sum\limits_{j:{l_{ji} \in E}}\;{{PR}_{j} \cdot {A\left\lbrack {j,i} \right\rbrack}}}}}$Or, written in matrix form:

$\overset{\longrightarrow}{PR} = {{\frac{ɛ}{n}\overset{->}{e}} + {\left( {1 - ɛ} \right)A\;\overset{\longrightarrow}{PR}}}$where {right arrow over (e)} is the vector of all 1's, and ε (0<ε<1) isthe probability parameter. In a preferred embodiment, the probabilityparameter ε is set to 0.15. Instead of computing an eigenvector, aJacobi iteration iterative method is used to resolve the equation.

The modified links analysis technique also uses at least one type ofre-ranking technique: (1) a score based re-ranking technique; and (2) anorder based re-ranking technique. The score based re-ranking techniqueuses a linear combination of content-based similarity score and thePageRank value of all web-pages:Score(w)=αSim+(1−α)PR(αε[0,1])where Sim is the content-based similarity between web-pages and querywords, and PR is the PageRank value.

The order based re-ranking technique is based on the rank orders of theweb-pages. Order based re-ranking is a linear combination of a positionof a pages in two lists. One list is sorted by similarity scores and theother list is sorted by PageRank values. That is,Score(w)=αO _(Sim)+(1−α)O _(PR)(αε[0, 1])where O_(Sim) and O_(PR) are positions of page w in similarity scorelist and PageRank list, respectively.

The foregoing Detailed Description has been presented for the purposesof illustration and description. Many modifications and variations arepossible in light of the above teaching. It is not intended to beexhaustive or to limit the subject matter described herein to theprecise form disclosed. Although the subject matter has been describedin language specific to structural features and/or methodological acts,it is to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims appendedhereto.

1. A method for augmenting initial search results from a search engine,comprising: using a computing device to perform the following:segmenting a user access log of the search engine into browsingsessions; generating ordered pairs of implicit links from the browsingsessions to generate an implicit links graph; determining a frequency ofeach of the ordered pairs of implicit links; filtering the ordered pairsof implicit links using a minimum support threshold to remove anyinfrequently occurring ordered pairs of implicit links to generatetwo-item sequential patterns; updating the implicit links graph usingthe two-item sequential patterns; and re-ranking the initial searchresults using the updated implicit links graph and a modified implicitlink analysis technique to generate and display enhanced search results.2. A computer-implemented method for generating page rankings using auser access log, comprising: segmenting the user access log intobrowsing sessions; extracting implicit links from the browsing sessions;generating an implicit links graph from the extracted implicit links;updating the implicit links graph using two-item sequential patternsobtained by filtering ordered pairs of implicit links; re-rankinginitial search results using the updated implicit links graph to obtainpage rankings; and displaying the page rankings to a user; wherein theimplicit links graph is a weighted direct graph that is described by theequation G′=(V,E′), where V is a set of vertices representing all pagesin a search space and E′ encompasses a set of implicit links between thepages.
 3. The computer-implemented method of claim 2, wherein extractingimplicit links further comprises using a two-item sequential patternmining technique to extract the implicit links from the browsingsessions.
 4. The computer-implemented method of claim 3, wherein thetwo-item sequential pattern mining technique further comprises moving agliding window over each explicit link path in the user access log. 5.The computer-implemented method of claim 4, further comprisinggenerating order pairs of pages using the gliding window.
 6. Thecomputer-implemented method of claim 5, wherein updating the implicitlinks graph further comprises setting all weights in the two-itemsequential patterns to zero.
 7. The computer-implemented method of claim6, further comprising adding a support to each of the weights.
 8. Thecomputer-implemented method of claim 2, further comprising defining agenerative model for the user access log based on the implicit linksgraph.
 9. The computer-implemented method of claim 2, wherein each ofthe implicit links further includes a conditional probability parameterof a page to be visited given a current page.
 10. Thecomputer-implemented method of claim 2, wherein extracting implicitlinks further comprises analyzing observed explicit links in the useraccess log.
 11. A computer-readable storage medium having stored thereoncomputer-executable instructions for performing the computer-implementedmethod recited in claim
 2. 12. A process for enhancing initial searchresults obtained from a search engine on a computer using a user accesslog, comprising: segmenting the user access log into browsing sessions;extracting implicit links of pages from the browsing session using atwo-item sequential pattern mining technique; generating an implicitlinks graph from the implicit links of pages; generating two-itemsequential patterns from the implicit links of pages; updating theimplicit links graph using the two-item sequential patterns to obtain anupdated implicit links graph; re-ranking the initial search resultsusing the updated implicit links graph and a modified implicit linkanalysis technique to generate updated search results; and displayingthe updated search results to a user.
 13. The process as set forth inclaim 12, further comprising: generating ordered pairs of pages from thesegmented user access log; and filtering the ordered pairs using aminimum support threshold to remove any ordered pairs that areinfrequently occurring in the user access log.
 14. The process as setforth in claim 12, wherein the modified implicit link analysis techniqueuses a modified re-ranking formula.
 15. The process as set forth inclaim 14, wherein the modified implicit link analysis technique uses atleast one of: (a) score-based re-ranking technique; (b) order-basedre-ranking technique.
 16. One or more computer-readable storage mediahaving computer-readable instructions stored thereon which, whenexecuted by one or more processors, cause the one or more processors toimplement the process of claim 12.